Adaptive Reduced Rank Regression
Authors: Qiong Wu, Felix MF Wong, Yanhua Li, Zhenming Liu, Varun Kanade
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our preliminary experiments confirm that our algorithm often out-performs existing baselines, and is always at least competitive. |
| Researcher Affiliation | Collaboration | Qiong Wu William & Mary Felix M. F. Wong Independent Researcher Yanhua Li Worcester Polytechnic Institute Zhenming Liu William & Mary Varun Kanade University of Oxford. Correspondence to: Qiong Wu <qwu05@email.wm.edu>. Currently at Google. |
| Pseudocode | Yes | Figure 1: Our algorithm (ADAPTIVE-RRR) for solving the regression y = Mx + ϵ. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes using a 'stock market dataset' and 'tweet data' but does not provide concrete access information (link, DOI, repository, or formal citation) for these datasets. |
| Dataset Splits | No | The paper refers to 'in-sample' and 'out-of-sample' data, implying a split, but does not specify the exact percentages or methodology for training, validation, and test splits. |
| Hardware Specification | No | The authors acknowledge William & Mary Research Computing for providing computational resources and technical support that have contributed to the results reported within this paper. This does not provide specific hardware models. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) used for the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for their model. |